Multilayer neural networks (MNN) are widely applied to the fields such as pattern recognition, image processing, functional approximation, and optimal computation. In recent years, due to the higher recognition accuracy and better parallelizability, multilayer artificial neural networks have received increasing attention by academic and industrial communities.
Different types of data generated in neural networks may be processed at different levels of precision. In other words, bit-widths for each data type may be set differently. Conventionally, a general-purpose processor configured to process data of a fixed bit-width, e.g., a 32-bit CPU, may be implemented to process the different types of neural network data. However, processing data of a shorter bit-width may lead to unnecessary power consumption.